Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood

Cheng Zheng, Zhi Geng
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:237-248, 2010.

Abstract

In this paper, we propose an approach for reconstructing asynchronous Boolean networks from observed data. We find the causal relationships in Boolean networks using an asynchronous evolution approach. In our approach, we first find a minimum explanatory set for a node to reduce complexity of candidate Boolean functions, and then we choose a Boolean function for the node based on the maximum likelihood. This approach is stimulated by the task SIGNET of the causal challenge #2 pot-luck (Jenkins, 2009). Besides the data set SIGNET, we also applied our approach to two other datasets to evaluate our approach: one is generated by Professor Isabelle Guyon and the other generated ourselves from the signal transduction network of Abscisic acid in guard cell.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-zheng10a, title = {Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood}, author = {Zheng, Cheng and Geng, Zhi}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {237--248}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/zheng10a/zheng10a.pdf}, url = {https://proceedings.mlr.press/v6/zheng10a.html}, abstract = {In this paper, we propose an approach for reconstructing asynchronous Boolean networks from observed data. We find the causal relationships in Boolean networks using an asynchronous evolution approach. In our approach, we first find a minimum explanatory set for a node to reduce complexity of candidate Boolean functions, and then we choose a Boolean function for the node based on the maximum likelihood. This approach is stimulated by the task SIGNET of the causal challenge #2 pot-luck (Jenkins, 2009). Besides the data set SIGNET, we also applied our approach to two other datasets to evaluate our approach: one is generated by Professor Isabelle Guyon and the other generated ourselves from the signal transduction network of Abscisic acid in guard cell.} }
Endnote
%0 Conference Paper %T Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood %A Cheng Zheng %A Zhi Geng %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-zheng10a %I PMLR %P 237--248 %U https://proceedings.mlr.press/v6/zheng10a.html %V 6 %X In this paper, we propose an approach for reconstructing asynchronous Boolean networks from observed data. We find the causal relationships in Boolean networks using an asynchronous evolution approach. In our approach, we first find a minimum explanatory set for a node to reduce complexity of candidate Boolean functions, and then we choose a Boolean function for the node based on the maximum likelihood. This approach is stimulated by the task SIGNET of the causal challenge #2 pot-luck (Jenkins, 2009). Besides the data set SIGNET, we also applied our approach to two other datasets to evaluate our approach: one is generated by Professor Isabelle Guyon and the other generated ourselves from the signal transduction network of Abscisic acid in guard cell.
RIS
TY - CPAPER TI - Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood AU - Cheng Zheng AU - Zhi Geng BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-zheng10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 237 EP - 248 L1 - http://proceedings.mlr.press/v6/zheng10a/zheng10a.pdf UR - https://proceedings.mlr.press/v6/zheng10a.html AB - In this paper, we propose an approach for reconstructing asynchronous Boolean networks from observed data. We find the causal relationships in Boolean networks using an asynchronous evolution approach. In our approach, we first find a minimum explanatory set for a node to reduce complexity of candidate Boolean functions, and then we choose a Boolean function for the node based on the maximum likelihood. This approach is stimulated by the task SIGNET of the causal challenge #2 pot-luck (Jenkins, 2009). Besides the data set SIGNET, we also applied our approach to two other datasets to evaluate our approach: one is generated by Professor Isabelle Guyon and the other generated ourselves from the signal transduction network of Abscisic acid in guard cell. ER -
APA
Zheng, C. & Geng, Z.. (2010). Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:237-248 Available from https://proceedings.mlr.press/v6/zheng10a.html.

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